Body Mass Index and Mortality in Chinese Older Adults -New Evidence from a Large Prospective Cohort in China

Association between dietary patterns and sarcopenia among community-dwelling older adults in five provinces of China: a cross-sectional study

Background

Sarcopenia is prevalent in older adults and not only severely affects their health, but also brings a greater economic burden on the patient's family as well as society. High-quality diet is one of influencing factors of sarcopenia, particularly important for muscle mass and function. This study aims to examine the dietary patterns of community-dwelling older adults in a typical region of China and explore the relationship between these dietary patterns and sarcopenia.

Methods

We used data of the Nutrition and Health Follow-up Study of the Chinese Population in 2021. Food frequency questionnaires were used to obtain food items intake frequency during the last year. Appendicular skeletal muscle mass (ASM), muscle strength and physical performance were assessed according to the Asian Sarcopenia Working Group (AWGS2019) criteria. Exploratory factor analysis was used to identify dietary patterns. Logistic regression models were used to examine the association between dietary patterns and sarcopenia.

Results

A total of 1,967 participants over the age of 65 were included in the study, and the prevalence of sarcopenia was 17.0%. According to the factor loadings of all of the 18 food groups, three dietary patterns were identified. These dietary patterns include the diversified dietary pattern, which is mainly characterized by the intake of soybeans, fungi and algae, animal meat, fruits, and legumes; the traditional dietary pattern, which is mainly defined by the consumption of rice, pork, poultry, vegetables, and aquatic products; and the wheat-based dietary pattern, which is mainly characterized by the intake of wheat, tubers, and other cereals. The diversified dietary pattern (OR = 0.54, p < 0.05) and the traditional dietary pattern (OR = 0.51, p < 0.05) were linked to a lower risk of developing sarcopenia, whereas the wheat-based dietary pattern (OR = 3.54, p < 0.05) was associated with a higher risk of sarcopenia. All three dietary patterns exhibited significantly correlated with muscle mass, grip strength, and physical performance (p < 0.05).

Conclusion

Dietary patterns are associated with sarcopenia in community-dwelling older adults in China. Adopting a healthy and sensible balanced diet and avoiding a single dietary preference may reduce the risk of sarcopenia in older adults.

In-hospital outcomes of older patients with gastric cancer and their risk factors: large comprehensive institution-based study

PURPOSE

Gastric cancer (GC) is mostly a disease of aging, and older patients with GC are generally frailer. This study aimed to describe the characteristics and in-hospital outcomes, both overall and stratified by gender and resection, and to explore factors associated with outcomes of first hospitalization, in older GC patients.

METHODS

Data on GC patients ≥ 65 years hospitalized from January 2016 until December 2020 were retrieved from the electronic medical records of a large tertiary hospital. Patient and tumor characteristics, duration and fee of hospitalization, and in-hospital mortality were described for overall patients and compared by gender and resection. Factors associated with outcomes of first hospitalization were explored using multivariable-adjusted logistic regression.

RESULTS

3238 eligible patients were analyzed, with a mean age of 71 years and a male proportion of 74%. The median duration and fee of first hospitalization were 13 days and 40,000 RMB, respectively, with a median fee of 17,000 RMB not covered by insurance. 16 (< 1%) and 32 (1%) deaths occurred during first and any hospitalization, respectively, with only 4 (< 1%) perioperative deaths. Compared to male patients, female cases had more often signet-ring-cell carcinoma, reduced food intake, resection, and history of major abdominal surgery. Compared to unresected cases, resected patients had higher body-mass-index and Barthel index, less often reduced food intake, weight loss, and risk of malnutrition, and more often common diet, longer hospital stay, and higher fee. Through multivariable-adjusted analysis, longer first hospital-stay was associated with earlier year of diagnosis, older ages, emergency admission, signet-ring-cell carcinoma, resection, history of anticoagulant intake, larger body-mass-index, non-common diet, and non-low-salt and non-diabetes diets; higher fee of first hospitalization was associated with later year of diagnosis, male gender, older ages, emergency admission, signet-ring-cell carcinoma, and resection.

CONCLUSIONS

In this large institution-based study, older GC patients had low in-hospital mortality rates; the insurance coverage needs to be improved. Several characteristics and in-hospital outcomes significantly differed by gender and resection status, and various factors associated with duration and fee of first hospitalization were identified, providing important hints for individualized and stratified geriatric GC care.

Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data

BACKGROUND AND OBJECTIVE

Real-world data encompass population diversity, enabling insights into chronic disease mortality risk among the elderly. Deep learning excels on large datasets, offering promise for real-world data. However, current models focus on single diseases, neglecting comorbidities prevalent in patients. Moreover, mortality is infrequent compared to illness, causing extreme class imbalance that impedes reliable prediction. We aim to develop a deep learning framework that accurately forecasts mortality risk from real-world data by addressing comorbidities and class imbalance.

METHODS

We integrated multi-task and cost-sensitive learning, developing an enhanced deep neural network architecture that extends multi-task learning to predict mortality risk across multiple chronic diseases. Each patient cohort with a chronic disease was assigned to a separate task, with shared lower-level parameters capturing inter-disease complexities through distinct top-level networks. Cost-sensitive functions were incorporated to ensure learning of positive class characteristics for each task and achieve accurate prediction of the risk of death from multiple chronic diseases.

RESULTS

Our study covers 15 prevalent chronic diseases and is experimented with real-world data from 482,145 patients (including 9,516 deaths) in Shenzhen, China. The proposed model is compared with six models including three machine learning models: logistic regression, XGBoost, and CatBoost, and three state-of-the-art deep learning models: 1D-CNN, TabNet, and Saint. The experimental results show that, compared with the other compared algorithms, MTL-CSDNN has better prediction results on the test set (ACC=0.99, REC=0.99, PRAUC=0.97, MCC=0.98, G-means = 0.98).

CONCLUSIONS

Our method provides valuable insights into leveraging real-world data for precise multi-disease mortality risk prediction, offering potential applications in optimizing chronic disease management, enhancing well-being, and reducing healthcare costs for the elderly population.

Development and validation of a nomogram for predicting anorexia of aging in older people

BACKGROUND

Anorexia of aging (AA) is a common geriatric syndrome that seriously endangers the health of older adults. Early identification of populations at risk of AAand the implementation of appropriate intervention measures hold significant public health importance. This study aimed to develop a nomogram for predicting the risk of AA among older people.

METHODS

We conducted a cross-sectional study involving 2144 community-dwelling older adults to evaluate the AA using the Simplified Nutritional Appetite Questionnaire. We utilized the Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis to select variables and develop a nomogram prediction model. The predictive performance of the nomogram was evaluated using the Receiver Operating Characteristic (ROC) curves, calibration curves, Decision Curve Analysis (DCA), and internal validation.

RESULTS

The prevalence of AA among Chinese older adults was 21.7% (95%CI: 20.0%-23.5%). Age, sex, family economic level, smoking status, dysphagia, loneliness, depressive symptoms, living alone, health literacy, life satisfaction, and body mass index have been identified as predictive factors for AA among older people. The nomogram constructed based on these predictive factors showed an area under the curve (AUC) of 0.766 (95%CI: 0.742-0.791), indicating good calibration and discrimination ability. Additionally, the results obtained from the 10-fold cross-validation process confirmed the nomogram's good predictive capabilities. Furthermore, the DCA results showed that the nomogram has clinical utility.

CONCLUSION

The nomogram constructed in this study serves as an effective tool for predicting anorexia of aging among community-dwelling older adults. Its implementation can help community healthcare workers evaluate the risk of AA in this population and identify high-risk groups.

Impact of obesity on intensive care unit outcomes in older patients with critical illness: A cohort study

BACKGROUND

Although the paradoxical association between obesity and improved survival has been reported in a variety of clinical settings, its applicability to intensive care unit (ICU) outcomes in older critically ill patients remains unclear. We sought to examine the association between obesity and 30-day mortality and other adverse outcomes in this population.

METHODS

We analyzed data of older patients (≥ 60 years) in the eICU Collaborative Research Database. Body mass index (BMI) was stratified according to the World Health Organization obesity classification. Logistic regression model was used to estimate adjusted odds ratios (ORs), and cubic spline curve was used to explore the nonlinear association between BMI and 30-day ICU outcomes. Stratified analysis and sensitivity analysis were also performed.

RESULTS

Compared with class I obesity, under- and normal-weight were associated with higher all-cause, cardiovascular and noncardiovascular mortality, and class III obesity was associated with greater all-cause and cardiovascular mortality (OR, 1.18 [95% CI, 1.06-1.32], 1.28 [1.08-1.51]). Obesity classes II and III were associated with higher composite all-cause mortality, mechanical ventilation, or vasoactive drug usage risks (OR, 1.12 [95% CI, 1.04-1.20], 1.33 [1.24-1.43]). Mechanical ventilation was strongly positively associated with BMI. A significant BMI-by-sex interaction was observed for cardiovascular mortality, such that the association between severe obesity and mortality was more pronounced among older men than older women.

CONCLUSIONS

The obesity paradox does not appear to apply to short-term ICU outcomes in older patients with critical illness, mainly due to increased all-cause and cardiovascular mortality in severely obese patients, particularly in men.

Association between Depression, Anxiety Symptoms and Gut Microbiota in Chinese Elderly with Functional Constipation

This study aimed to investigate the relationship between anxiety, depression, and gut microbiota in elderly patients with FC.

METHODS

in this cross-sectional study, a total of 198 elderly participants (85 male and 113 female) aged over 60 years were recruited. The study was conducted in Changsha city, China. The participants completed an online questionnaire, including The Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), The Patient Assessment of Symptoms (PAC-SYM), and The Patient Assessment of Quality of Life (PAC-QoL). We selected the 16S rDNA V3 + V4 region as the amplification region and sequenced the gut microbiota using the Illumina Novaseq PE250 high-throughput sequencing platform.

RESULTS

in total, 30.3% of patients with constipation had depression, while 21.3% had anxiety. The relative abundance of intestinal microbiota in the normal group was higher than that in the anxiety and depression group. According to LEfSe analysis, the relative abundance of g_Peptoniphilus and g_Geobacter in the people without depression and anxiety was higher. The relative abundance of g_Pseudoramibacter-Eubacterium and g_Candidatus-Solibacter in the depression group was lower, and the relative abundance of g_Bacteroides and g_Paraprevotella, g_Cc_115 in the anxiety group was higher. In addition, according to the correlation analysis, g_Aquicella and g_Limnohabitans were negatively correlated with constipation symptoms, anxiety, and depression.

CONCLUSIONS

this study found that gut microbiota composition may be associated with a higher incidence of anxiety and depression in patients with FC, thus providing insight into the mechanisms that ameliorate mood disorders in patients with FC.